DD2601 Deep Generative Models and Synthesis 7.5 credits
Information per course offering
Information for Autumn 2025 Start 25 Aug 2025 programme students
- Course location
KTH Campus
- Duration
- 25 Aug 2025 - 24 Oct 2025
- Periods
Autumn 2025: P1 (7.5 hp)
- Pace of study
50%
- Application code
50363
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Max: 50
- Target group
- Open to all master's programmes as long as it can be included in the programme.
- Planned modular schedule
- No information inserted
- Schedule
Contact
Course syllabus and course offering missing
Course syllabus missing, showing available course information. Course offering also missing for current semester as well as for previous and coming semesters
Content and learning outcomes
Course contents
No information inserted
Intended learning outcomes
No information inserted
Literature and preparations
Specific prerequisites
No information inserted
Recommended prerequisites
- Good programming skills (equiv. to DD1337/DD1310–1319/DD1331/DD1332/ID1018) including Python, PyTorch, Jupyter Notebooks.
- Probability theory (equiv. to SF1900–SF1935) including probability, conditional probability, Bayes’ law, independence, expectation, random variables, probability mass and density functions, samples, random sampling, mean, variance, standard deviation, median, correlation, covariance, uniform distributions, multivariate Gaussian distributions and their properties, conditional expectation, parameter estimation, maximum-likelihood estimation, biassed estimators, consistency, change of variables, Jensen’s inequality, least-squares regression.
- Algebra and geometry (equiv. to SF1624) including vectors, matrices, systems of linear equations, inner and outer products, norms, triangle inequality, metric spaces, determinants, eigenvalues, linear dependence, subspaces, trace of a matrix.
- Single-variable calculus (equiv. to SF1625) including functions, domains, ranges, monotonicity, exponential functions and logarithms, limits, l'Hôpital's rule, sequences, change of variables, convex functions, ordinary differential equations, Euler’s method.
- Multivariate calculus (equiv. to SF1626/SF1674) including partial derivatives, multivariate chain rule, change of variables, gradients, Hessian matrices, Jacobian matrices.
- Machine learning (equiv. to DD1420/DD2421 or DD2380/ID1214) including optimisation, convexity, loss functions, train/val/test sets, k-fold cross validation, mean squared error, classification, accuracy, overfitting, Bayes-optimal error rate, Gaussian mixture models, high-dimensional geometry (curse of dimensionality). Information theory for machine learning including entropy, bits, differential entropy, cross-entropy.
- Deep learning (equiv. to DD2424/DD2437) including feed-forward networks, activation functions, ReLU, softmax, stochastic gradient descent, updates, epochs, CNNs, RNNs, mean and variance normalisation, initialisation, hyperparameters.
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
A, B, C, D, E, FX, F
Examination
No information inserted
Examiner
No information inserted
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Computer Science and Engineering
Education cycle
Second cycle
Supplementary information
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex